What if human performance isn’t changing — it already changed?
Every so often, the ground shifts beneath our feet, and we only notice once we’ve crossed the fault line. That’s how the human performance industry feels right now. What used to be called a “strength coach” became “S&C coach,” and now “human performance professional,” “specialist” or “director.” The job title got bigger, and so did the scope. We moved from coaching strength training (i.e. lifting weights), to conditioning for game-day readiness, to… everything. Recovery. Sleep. Cognition. Hormones. Force-time curves. Load monitoring. Leadership development. Culture building. It’s not just more, it’s a lot. And I’m not sure when or if we stopped to ask what all that more is actually doing for us and the people we support. Being in this field for over 40 years has been long enough to see the arc from physical education to exercise/sports science to complex systems modeling. From Douglas bags in the lab to force plates in high school weight rooms; from physical literacy to tech and data literacy. If I’m being honest, it feels like we’ve been riding the wave, but not always steering. We’ve experienced this incredible convergence of disciplines: physiology, psychology, biomechanics, neuroscience, data science, even sociology. Add in wearable tech and computing power that can capture every breath, step and spike, and now the job doesn’t just require deep knowledge – it demands range. But range without reflection can get noisy. For several years I’ve struggled with: Are we evolving… or just chasing? Are we getting better… or just busier? Are we hedgehogs focused on core truths or foxes sometimes darting after every shiny new thing? I’m not sure if we need to be more like hedgehogs or foxes right now. Maybe both, or maybe just more honest about when we’re being one or the other. “Old school,” not “just old” I think “old school” is sometimes confused or mistaken with “just old.” And there’s a difference. Old school, as I’ve experienced it from both sides, is rooted in ground-truth experience – people who were there in the earlier days when principles were still being forged, in the lab and on the field. That kind of thinker and practitioner brings something you can’t teach quickly – not just pattern recognition, but a lived sense of what matters and what doesn’t. But here’s what really defines the best of this generation: in the best cases, we know our own biases. We’ve earned a worldview through experience, but we’re willing to update our judgment when new information makes it necessary. That’s not being stuck in the past. That’s Bayesian thinking before we had a name for it. At the same time, I’m impressed and motivated by what newer, science-forward professionals are bringing. The resolution can be sharper, the calibration is improving, and the feedback loops are tighter. When we ask the right questions, we can learn faster, and on a larger scale. What concerns me is when the technology or role title comes first, before the question or the mission. Expertise matters, but so does cross-talk. Sometimes the smartest move is to know where you are, and grow by working with those who see things differently. Old school isn’t just old. It’s earned perspective and updated with awareness. New centers of gravity One thing that feels different now is how many new centers of gravity have emerged in the human performance conversation. Data is one of them. But so is recovery. So is cognitive performance. So is longevity. What used to be peripheral topics or afterthoughts are now front and center in conference talks, podcasts, program briefs and team meetings. And it’s not that these things don’t matter – they do. The question is whether we’re making them meaningful or just making them louder. With data, you see the extremes: some professionals latch onto anything that can be measured, assuming that metrics equal mastery. Others reject it outright, relying on feel, pattern recognition, and an earned skepticism of hype cycles. Still others sit on the sidelines, blaming the systems of academic, commercial, or institutional for being out of touch or solely profit-driven. But the real tension isn’t whether these topics matter. It’s whether we know how to extract signals from noise and what to do with either. Tech, data, and complex systems thinking are like a kettlebell: in the right hands, with the right mindset and timing, it’s a powerful tool. In the wrong hands, it’s just a doorstop. Where to apply focus The smartest move we can make as professionals is to just stop and take a breath (and yes, breath is now a topic of research, debate and commercial packaging too). Really pause for a moment. Ask yourself three questions: What do I love doing? What am I good at? And what can I make a good living at? If all three line up, great. But in my experience, that’s rare. The hard part is being brutally honest with yourself and then asking someone you trust to be brutally honest with your answers too. Because here’s a truth: we can’t all be SMEs in everything. But if we each choose a couple areas to dig deeply into and stay aware of the adjacent disciplines that support our work, then we can do something even more valuable: We become people who recognize first principles, collaborate well, and build trust across the ecosystem. That’s one quality that makes a good teammate. And good teammates are the foundation of any high-performance system, not just data sets or job titles. Where’s this all heading? Honestly, I’m not sure. I think the better question might be: How are we showing up while it’s all happening? If you’re feeling overwhelmed by it, good. That might just mean you’re taking it seriously. In fact, if you’re not overwhelmed from time to time, I’d argue you’re probably not working hard enough on your craft. Slow it down. Ask questions. Reach out to someone outside your lane. Leave a comment. Email me directly. Push back if you disagree;